* Update cluster GCN README
We do not need to build metis right now.
We can use builtin metis.
* Fix gcmc mxnet
* Fix graphwriter
Co-authored-by: Ubuntu <ubuntu@ip-172-31-68-185.ec2.internal>
* slice dstdata from srcdata within nn module
* a bunch of fixes
* add comment
* fix gcmc layer
* repr for blocks
* fix
* fix context
* fix
* do not copy internal columns
* docstring
* add standalone mode
* add comments.
* add tests for sampling.
* fix.
* make the code to run the standalone mode
* fix
* fix
* fix readme.
* fix.
* fix test
Co-authored-by: Chao Ma <mctt90@gmail.com>
* initial version from distributed training.
This is copied from multiprocessing training.
* modify for distributed training.
* it's runnable now.
* measure time in neighbor sampling.
* simplify neighbor sampling.
* fix a bug in distributed neighbor sampling.
* allow single-machine training.
* fix a bug.
* fix a bug.
* fix openmp.
* make some improvement.
* fix.
* add prepare in the sampler.
* prepare nodeflow async.
* fix a bug.
* get id.
* simplify the code.
* improve.
* fix partition.py
* fix the example.
* add more features.
* fix the example.
* allow one partition
* use distributed kvstore.
* do g2l map manually.
* fix commandline.
* a temp script to save reddit.
* fix pull_handler.
* add pytorch version.
* estimate the time for copying data.
* delete unused code.
* fix a bug.
* print id.
* fix a bug
* fix a bug
* fix a bug.
* remove redundent code.
* revert modify in sampler.
* fix temp script.
* remove pytorch version.
* fix.
* distributed training with pytorch.
* add distributed graph store.
* fix.
* add metis_partition_assignment.
* fix a few bugs in distributed graph store.
* fix test.
* fix bugs in distributed graph store.
* fix tests.
* remove code of defining DistGraphStore.
* fix partition.
* fix example.
* update run.sh.
* only read necessary node data.
* batching data fetch of multiple NodeFlows.
* simplify gcn.
* remove unnecessary code.
* use the new copy_from_kvstore.
* update training script.
* print time in graphsage.
* make distributed training runnable.
* use val_nid.
* fix train_sampling.
* add distributed training.
* add run.sh
* add more timing.
* fix a bug.
* save graph metadata when partition.
* create ndata and edata in distributed graph store.
* add timing in minibatch training of GraphSage.
* use pytorch distributed.
* add checks.
* fix a bug in global vs. local ids.
* remove fast pull
* fix a compile error.
* update and add new APIs.
* implement more methods in DistGraphStore.
* update more APIs.
* rename it to DistGraph.
* rename to DistTensor
* remove some unnecessary API.
* remove unnecessary files.
* revert changes in sampler.
* Revert "simplify gcn."
This reverts commit 0ed3a34ca714203a5b45240af71555d4227ce452.
* Revert "simplify neighbor sampling."
This reverts commit 551c72d20f05a029360ba97f312c7a7a578aacec.
* Revert "measure time in neighbor sampling."
This reverts commit 63ae80c7b402bb626e24acbbc8fdfe9fffd0bc64.
* Revert "add timing in minibatch training of GraphSage."
This reverts commit e59dc8957a414c7df5c316f51d78bce822bdef5e.
* Revert "fix train_sampling."
This reverts commit ea6aea9a4aabb8ba0ff63070aa51e7ca81536ad9.
* fix lint.
* add comments and small update.
* add more comments.
* add more unit tests and fix bugs.
* check the existence of shared-mem graph index.
* use new partitioned graph storage.
* fix bugs.
* print error in fast pull.
* fix lint
* fix a compile error.
* save absolute path after partitioning.
* small fixes in the example
* Revert "[kvstore] support any data type for init_data() (#1465)"
This reverts commit 87b6997bf2.
* fix a bug.
* disable evaluation.
* Revert "Revert "[kvstore] support any data type for init_data() (#1465)""
This reverts commit f5b8039c6326eb73bad8287db3d30d93175e5bee.
* support set and init data.
* support set and init data.
* Revert "Revert "[kvstore] support any data type for init_data() (#1465)""
This reverts commit f5b8039c6326eb73bad8287db3d30d93175e5bee.
* fix bugs.
* fix unit test.
* move to dgl.distributed.
* fix lint.
* fix lint.
* remove local_nids.
* fix lint.
* fix test.
* remove train_dist.
* revert train_sampling.
* rename funcs.
* address comments.
* address comments.
Use NodeDataView/EdgeDataView to keep track of data.
* address comments.
* address comments.
* revert.
* save data with DGL serializer.
* use the right way of getting shape.
* fix lint.
* address comments.
* address comments.
* fix an error in mxnet.
* address comments.
* add edge_map.
* add more test and fix bugs.
Co-authored-by: Zheng <dzzhen@186590dc80ff.ant.amazon.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-6-131.us-east-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-26-167.us-east-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-150.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-250.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-30-135.us-west-2.compute.internal>
* control variate first commit
* bug fixes
* split to single and multi GPU
* update readme
* bugfix
* bugfix
* remove push
* bugfix on multi gpu
* update README
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* remove edge and to bipartite and graphsage with sampling
* fixes
* fixes
* fixes
* reenable multigpu training
* fixes
* compatibility in DGLGraph
* rename to compact_as_bipartite
* bugfix
* lint
* add offline inference
* skip GPU tests
* fix
* addresses comments
* fix
* fix
* fix
* more tests
* more docs and unit tests
* workaround for empty slice on empty data
* upd
* fig edgebatch edges
* add test
* trigger
* Update README.md for pytorch PinSage example.
Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.
* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.
1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
test/pytorch/test_nn.py work on both CPU and GPU
* Fix style
* Delete unused code
* Make agnostic test only related to tests/backend
1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu
* Fix code style
* fix
* doc
* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.
* Fix syntex
* Remove rand
* Add TAGCN nn.module and example
* Now tagcn can run on CPU.
* Add unitest for TGConv
* Fix style
* For pubmed dataset, using --lr=0.005 can achieve better acc
* Fix style
* Fix some descriptions
* trigger
* Fix doc
* Add nn.TGConv and example
* Fix bug
* Update data in mxnet.tagcn test acc.
* Fix some comments and code
* delete useless code
* Fix namming
* Fix bug
* Fix bug
* Add test for mxnet TAGCov
* Add test code for mxnet TAGCov
* Update some docs
* Fix some code
* Update docs dgl.nn.mxnet
* Update weight init
* Fix
* reproduce the bug
* Fix concurrency bug reported at #755.
Also make test_shared_mem_store.py more deterministic.
* Update test_shared_mem_store.py
* Update dmlc/core
* networkx >= 2.4 will break our examples
* Update tutorials/requirements
* fix selfloop edges
* upd version
Zihao Ye